scholarly journals Model-based Bayesian inference of the ventilation distribution in patients with Cystic Fibrosis from multiple breath washout, with comparison to ventilation MRI

2021 ◽  
Author(s):  
Carl A. Whitfield ◽  
Alexander Horsley ◽  
Oliver E. Jensen ◽  
Felix C. Horn ◽  
Guilhem J. Collier ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Bayesian Method ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Acquisition Time ◽  
Model Parameters ◽  
Imaging Data ◽  
Ventilation Distribution ◽  
Pixel Intensity ◽  
Multiple Breath Washout

AbstractBackgroundIndices of ventilation heterogeneity (VH) from multiple breath washout (MBW) have been shown to correlate well with VH indices derived from hyperpolarised gas ventilation MRI. Here we report the prediction of ventilation distributions from MBW data using a mathematical model, and the comparison of these predictions with imaging data.MethodsWe developed computer simulations of the ventilation distribution in the lungs to model MBW measurement with 3 parameters: σV, determining the extent of VH; V0, the lung volume; and VD, the dead-space volume. These were inferred for each individual from supine MBW data recorded from 25 patients with cystic fibrosis (CF) using approximate Bayesian computation. The fitted models were used to predict the distribution of gas imaged by 3He ventilation MRI measurements collected from the same visit.ResultsThe MRI indices measured (I1/3, the fraction of pixels below one-third of the mean intensity and ICV, the coefficient of variation of pixel intensity) correlated strongly with those predicted by the MBW model fits (r = 0.93, 0.87 respectively). There was also good agreement between predicted and measured MRI indices (mean bias ± limits of agreement: I1/3: 0.002 ± 0.112 and ICV: −0.001 ± 0.293). Fitted model parameters were robust to truncation of MBW data.ConclusionWe have shown that the ventilation distribution in the lung can be inferred from an MBW signal, and verified this using ventilation MRI. The Bayesian method employed extracts this information with fewer breath cycles than required for LCI, reducing acquisition time required, and gives uncertainty bounds, which are important for clinical decision making.New and NoteworthyThis paper demonstrates that the ventilation distribution observed by ventilation MRI in cystic fibrosis patients can be inferred using multiple breath washout data. The Bayesian method used quantifies prediction uncertainty. This has the potential to be used in the analysis of washout data in the clinic to give greater physiological insight more efficiently. The predictions also remained robust to truncation of the washout dataset, meaning that data-capture time can be significantly reduced using this method.

scholarly journals Patterns of regional lung physiology in cystic fibrosis using ventilation magnetic resonance imaging and multiple-breath washout

2018 ◽  
Vol 52 (5) ◽  
pp. 1800821 ◽  
Author(s):  
Laurie J. Smith ◽  
Guilhem J. Collier ◽  
Helen Marshall ◽  
Paul J.C. Hughes ◽  
Alberto M. Biancardi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cystic Fibrosis ◽  
Total Lung Capacity ◽  
Resonance Imaging ◽  
Ventilation Distribution ◽  
Lung Clearance ◽  
Lung Capacity ◽  
Lung Clearance Index ◽  
Ventilation Heterogeneity ◽  
Multiple Breath Washout

Hyperpolarised helium-3 (3He) ventilation magnetic resonance imaging (MRI) and multiple-breath washout (MBW) are sensitive methods for detecting lung disease in cystic fibrosis (CF). We aimed to explore their relationship across a broad range of CF disease severity and patient age, as well as assess the effect of inhaled lung volume on ventilation distribution.32 children and adults with CF underwent MBW and 3He-MRI at a lung volume of end-inspiratory tidal volume (EIVT). In addition, 28 patients performed 3He-MRI at total lung capacity. 3He-MRI scans were quantitatively analysed for ventilation defect percentage (VDP), ventilation heterogeneity index (VHI) and the number and size of individual contiguous ventilation defects. From MBW, the lung clearance index, convection-dependent ventilation heterogeneity (Scond) and convection–diffusion-dependent ventilation heterogeneity (Sacin) were calculated.VDP and VHI at EIVT strongly correlated with lung clearance index (r=0.89 and r=0.88, respectively), Sacin (r=0.84 and r=0.82, respectively) and forced expiratory volume in 1 s (FEV1) (r=−0.79 and r=−0.78, respectively). Two distinct 3He-MRI patterns were highlighted: patients with abnormal FEV1 had significantly (p<0.001) larger, but fewer, contiguous defects than those with normal FEV1, who tended to have numerous small volume defects. These two MRI patterns were delineated by a VDP of ∼10%. At total lung capacity, when compared to EIVT, VDP and VHI reduced in all subjects (p<0.001), demonstrating improved ventilation distribution and regions of volume-reversible and nonreversible ventilation abnormalities.

Gallbladder Polyps: Real or Imagined?

2018 ◽  
Vol 84 (10) ◽  
pp. 1670-1674 ◽  
Author(s):  
Yiping Li ◽  
Talar Tejirian ◽  
J. Craig Collins
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Imaging Data ◽  
Final Pathology ◽  
Gallbladder Polyps ◽  
Imaging Results ◽  
Unreliable Method ◽  
Us Findings ◽  
Risk Of Cancer ◽  
Selection Of

The finding of gallbladder polyps on imaging studies prompts further workup. Imaging results are often discordant with final pathology. The goal of this study is to compare polypoid lesions of the gallbladder found on preoperative ultrasound (US) with final pathologic diagnosis after cholecystectomy to help guide clinical decision-making. A retrospective study was conducted identifying adult patients who were diagnosed with polyps via US and who underwent cholecystectomy from 2008 through 2015. Imaging data, final pathology, and demographics were manually reviewed. A total of 2290 cholecystectomy patients had US-based polyps. Of these, 1661 patients (73%) did not have polyps on final pathology; primarily, stones or sludge were identified. Adenomyosis was diagnosed in 61 patients (2.7%). A total of 556 patients (24.2%) had pathologic polypoid lesions with the following breakdown: 463 (20.2%) cholesterol polyps, 43 other benign polyps (1.8%), 40 adenomas (1.7%), and 10 adenocarcinomas (0.4%). All patients with adenocarcinoma were older than 40 years and 91 per cent had US findings of polyps >10 mm. Ultrasound alone is an unreliable method of detecting real gallbladder polyps. This large database study found a very low risk of cancer. Size on US and patient age should be considered in the selection of appropriate surgical candidates with sonographic “polyps.”

Complementing sparse vascular imaging data by physiological adaptation rules

2020 ◽  
Author(s):  
Maarten H.G. Heusinkveld ◽  
Robert J. Holtackers ◽  
Bouke P. Adriaans ◽  
Jos Op't Roodt ◽  
Theo Arts ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Mechanical Load ◽  
Wall Stress ◽  
Clinical Decision ◽  
Physiological Adaptation ◽  
Patient Specific ◽  
Data Sets ◽  
Imaging Data ◽  
Artery Segment ◽  
Flow Waveforms

Introduction:Mathematical modeling of pressure and flow waveforms in blood vessels using pulse wave propagation (PWP)-models has tremendous potential to support clinical decision-making. For a personalized model outcome, measurements of all modeled vessel radii and wall thicknesses are required. In clinical practice, however, data sets are often incomplete. To overcome this problem, we hypothesized that the adaptive capacity of vessels in response to mechanical load could be utilized to fill in the gaps of incomplete patient-specific data sets. Methods:We implemented homeostatic feedback loops in a validated PWP model to allow adaptation of vessel geometry to maintain physiological values of wall stress and wall shear stress. To evaluate our approach, we gathered vascular MRI and ultrasound data sets of wall thicknesses and radii of central and arm arterial segments of ten healthy subjects. Reference models (i.e. termed RefModel, n=10) were simulated using complete data, whereas adapted models (AdaptModel, n=10) used data of one carotid artery segment only while the remaining geometries in this model were estimated using adaptation. We evaluated agreement between RefModel and AdaptModel geometries, as well as between pressure and flow waveforms of both models. Results:Limits of agreement (bias±2SD of difference) between AdaptModel and RefModel radii and wall thicknesses were 0.2±2.6 mm and -140±557 μm, respectively. Pressure and flow waveform characteristics of the AdaptModel better resembled those of the RefModels as compared to the model in which the vessels were not adapted.Conclusions:Our adaptation-based PWP-model enables personalization of vascular geometries even when not all required data is available.

scholarly journals Radiogenomics and Radiomics in Liver Cancers

Diagnostics ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 4 ◽  
Author(s):  
Aman Saini ◽  
Ilana Breen ◽  
Yash Pershad ◽  
Sailendra Naidu ◽  
M. Knuttinen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Information ◽  
Clinical Decision ◽  
Molecular Profile ◽  
Imaging Features ◽  
Imaging Data ◽  
Prognostic Information ◽  
Cross Sectional ◽  
Related Field ◽  
Liver Cancers

Radiogenomics is a computational discipline that identifies correlations between cross-sectional imaging features and tissue-based molecular data. These imaging phenotypic correlations can then potentially be used to longitudinally and non-invasively predict a tumor’s molecular profile. A different, but related field termed radiomics examines the extraction of quantitative data from imaging data and the subsequent combination of these data with clinical information in an attempt to provide prognostic information and guide clinical decision making. Together, these fields represent the evolution of biomedical imaging from a descriptive, qualitative specialty to a predictive, quantitative discipline. It is anticipated that radiomics and radiogenomics will not only identify pathologic processes, but also unveil their underlying pathophysiological mechanisms through clinical imaging alone. Here, we review recent studies on radiogenomics and radiomics in liver cancers, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and metastases to the liver.

scholarly journals Bayesian network modelling for early diagnosis and prediction of Endometriosis

2020 ◽  
Author(s):  
Rachel Collins ◽  
Norman Fenton
Keyword(s):  
Early Diagnosis ◽  
Graphical Models ◽  
Observational Studies ◽  
Clinical Decision Making ◽  
Causal Structure ◽  
Clinical Decision ◽  
Underlying Disease ◽  
Model Parameters ◽  
Network Modelling ◽  
The Impact

AbstractBayesian networks (BNs) are graphical models that can combine knowledge with data to represent the causal probabilistic relationships between a set of variables and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. This paper describes a BN causal model for the early diagnosis and prediction of endometriosis. The causal structure of the BN model is developed using an idioms-based approach and the model parameters are derived from the data reported in multiple medical observational studies and trials. The BN incorporates the impact of errors and omissions in reporting endometriosis, and the distinction between assumed and actual cases. Hence, it is also able to explain both flawed and counterintuitive results of observational studies.

scholarly journals Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 3944
Author(s):  
Daniele Corradini ◽  
Leonardo Brizi ◽  
Caterina Gaudiano ◽  
Lorenzo Bianchi ◽  
Emanuela Marcelli ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Future Perspective ◽  
Imaging Data ◽  
Mr Images ◽  
Evaluation Time ◽  
Inter Observer Variability ◽  
Clinically Significant

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.

scholarly journals The cystic fibrosis lung microenvironment alters antibiotic activity: causes and effects

2021 ◽  
Vol 30 (161) ◽  
pp. 210055
Author(s):  
Sara Van den Bossche ◽  
Emma De Broe ◽  
Tom Coenye ◽  
Eva Van Braeckel ◽  
Aurélie Crabbé
Keyword(s):  
Cystic Fibrosis ◽  
Antibiotic Treatment ◽  
Antibiotic Susceptibility ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Antibiotic Activity ◽  
Population Diversity ◽  
Tract Infections

Chronic airway colonisation by Pseudomonas aeruginosa, a hallmark of cystic fibrosis (CF) lung disease, is associated with increased morbidity and mortality and despite aggressive antibiotic treatment, P. aeruginosa is able to persist in CF airways. In vitro antibiotic susceptibility assays are poor predictors of antibiotic efficacy to treat respiratory tract infections in the CF patient population and the selection of the antibiotic(s) is often made on an empirical base. In the current review, we discuss the factors that are responsible for the discrepancies between antibiotic activity in vitro and clinical efficacy in vivo. We describe how the CF lung microenvironment, shaped by host factors (such as iron, mucus, immune mediators and oxygen availability) and the microbiota, influences antibiotic activity and varies widely between patients. A better understanding of the CF microenvironment and population diversity may thus help improve in vitro antibiotic susceptibility testing and clinical decision making, in turn increasing the success rate of antibiotic treatment.

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